Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting

## -- Attaching packages -------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.2
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0

## -- Conflicts ----------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(googlesheets4) # for reading googlesheet data
library(lubridate)     # for date manipulation

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(openintro)     # for the abbr2state() function

## Loading required package: airports

## Loading required package: cherryblossom

## Loading required package: usdata

library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

library(ggmap)         # for mapping points on maps

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(gplots)        # for col2hex() function

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
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##     lowess

library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
gs4_deauth()           # To not have to authorize each time you knit.
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

## Parsed with column specification:
## cols(
##   Brand = col_character(),
##   `Store Number` = col_character(),
##   `Store Name` = col_character(),
##   `Ownership Type` = col_character(),
##   `Street Address` = col_character(),
##   City = col_character(),
##   `State/Province` = col_character(),
##   Country = col_character(),
##   Postcode = col_character(),
##   `Phone Number` = col_character(),
##   Timezone = col_character(),
##   Longitude = col_double(),
##   Latitude = col_double()
## )

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   state = col_character(),
##   fips = col_character(),
##   cases = col_double(),
##   deaths = col_double()
## )

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

simple_starbucks <- Starbucks %>% 
  rename(ownership = "Ownership Type")

world <- get_stamenmap(
  bbox = c(left = -180, bottom = -57, right = 179, top = 82.1),
           maptype = "terrain",
           zoom = 2)

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ggmap(world)+
  geom_point(data = simple_starbucks,
             aes(x = Longitude, y = Latitude, color = ownership),
             alpha = .3,
             size = .1) +
  scale_color_viridis_d("viridis")+
  theme_map()+
  labs(title = "Starbucks by Ownership Type")

## Warning: Removed 1 rows containing missing values (geom_point).

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

twin_cities_map <- get_stamenmap(
  bbox = c(left = -93.5252, bottom = 44.8262, right = -92.8867, 
           top = 45.0958),
  maptype = "terrain",
  zoom = 11)

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ggmap(twin_cities_map) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             alpha = 3,
             size = 1)+
  theme_map()+
  labs(title = "Starbucks Locations in Twin Cities")

## Warning: Removed 25499 rows containing missing values (geom_point).

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

twin_cities_map <- get_stamenmap(
  bbox = c(left = -93.5252, bottom = 44.8262, right = -92.8867, 
           top = 45.0958),
  maptype = "terrain",
  zoom = 5)

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ggmap(twin_cities_map) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             alpha = .3,
             size = .1)+
  theme_map()+
  labs(title = "Starbucks Locations in Twin Cities")

## Warning: Removed 25499 rows containing missing values (geom_point).

It lessens/heightens the detail on the map; the smaller the number the less detail, the higher the number the more detail. In the tutorial it said be careful, cause the more detail the longer it takes to load. I used 11 on the previous map because that was the zoom on the openstreetmap.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

twin_cities_map <- get_stamenmap(
  bbox = c(left = -93.5252, bottom = 44.8262, right = -92.8867, 
           top = 45.0958),
  maptype = "watercolor",
  zoom = 11)

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ggmap(twin_cities_map) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             alpha = 3,
             size = 3)+
  theme_map()+
  labs(title = "Starbucks Locations in Twin Cities")

## Warning: Removed 25499 rows containing missing values (geom_point).

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

twin_cities_map <- get_stamenmap(
  bbox = c(left = -93.5252, bottom = 44.8262, right = -92.8867, 
           top = 45.0958),
  maptype = "terrain",
  zoom = 11)
ggmap(twin_cities_map) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             alpha = 3,
             size = 3)+
  theme_map()+
  annotate("text", x = -93.1593, y = 44.9420, label = "Macalester College")+
  labs(title = "Starbucks Locations in Twin Cities")

## Warning: Removed 25499 rows containing missing values (geom_point).

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

## Parsed with column specification:
## cols(
##   state = col_character(),
##   est_pop_2018 = col_double()
## )

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does. You’ve created a new data set for census_pop_est_2018 so that it only shows state–which are all lowercase to match state_name in Starbucks data–and then you left_join(ed) census_pop_est_2018 data to the starbucks_us_by_state data set to create one data set named starbucks_with_2018_pop_est. Within this new data set, you took the number of starbucks, divided by estimated population, and multiplied that product byt 10000, to get a new variable for each state that says the number of Starbucks location per 10000 people

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

us_map <- map_data("state")

starbucks_us <- Starbucks %>% 
  filter(Country == "US") %>% 
  filter(`State/Province` != "AK") %>% 
  filter(`State/Province` != "HI")
  
  starbucks_with_2018_pop_est%>% 
  ggplot() +
  geom_map(map = us_map,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  geom_point(data = starbucks_us,
             aes(x = Longitude, y = Latitude),
             size = .05,
             alpha = .2, 
             color = "yellow") +
  expand_limits(x = us_map$long, y = us_map$lat) + 
  labs(title = "Starbucks in the USA",
       caption = "BY:Cheyenne Woerman") +
  theme_map() +
  theme(legend.background = element_blank())

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

  • Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

favorite_stp_by_chey <- tibble(
  place = c("Home", "Macalester College", "SpyHouse Coffee", 
            "Rockford, IL", "Spartan House", "Banana Split",
            "Oswego High School", "Ulta", "Wet Paint", "Sencha Tea"),
  long = c(-88.33368, -93.1712321, -93.16808,
           -89.3875, -88.3465, -88.29924, 
           -88.34329, -88.3180, -93.17299, -93.17329),
  lat = c(41.70668, 44.9378965, 44.92843,
          42.0998, 41.7479, 41.77444, 
          41.68169, 41.6996, 44.93941, 44.93943))

leaflet(data = favorite_stp_by_chey) %>% 
    addProviderTiles(providers$Stamen.Terrain) %>% 
    addCircles(lng = ~long,
               lat = ~lat, 
               label = ~place,
               weight = 20,
               opacity = .5,
               color = col2hex("lightgreen")) %>% 
    addPolylines(lng = ~long,
                 lat = ~lat,
                 color = col2hex("darkblue")) %>% 
  addLegend("bottomleft", title = "Chey's Favorite Stops",labels = "place", colors = col2hex("lightgreen"))

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

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## cols(
##   name = col_character(),
##   lat = col_double(),
##   long = col_double(),
##   nbBikes = col_double(),
##   nbEmptyDocks = col_double()
## )

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

stations_new <- Stations %>% 
  left_join(Trips,
            by = c("name" = "sstation")) %>% 
  group_by(name, lat, long) %>% 
  summarize(count = n())

## `summarise()` regrouping output by 'name', 'lat' (override with `.groups` argument)

  washington_dc <- get_stamenmap(
    bbox = c(left = -77.6747, bottom = 38.5976, right = -76.3976, top = 39.1908),
    maptype = "terrain-background",
    zoom = 12)

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ggmap(washington_dc) +
  geom_point(data = stations_new, 
             aes(x = long, y = lat, color = count))+
  labs(title = "Total # of Departures")

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

stations_new <- Stations %>% 
  left_join(Trips,
            by = c("name" = "sstation")) %>% 
  group_by(name, lat, long) %>% 
  summarize(percent_casual = mean(client == "Casual")) 

## `summarise()` regrouping output by 'name', 'lat' (override with `.groups` argument)

  washington_dc <- get_stamenmap(
    bbox = c(left = -77.6747, bottom = 38.5976, right = -76.3976, top = 39.1908),
    maptype = "terrain-background",
    zoom = 12)

## 150 tiles needed, this may take a while (try a smaller zoom).

ggmap(washington_dc) +
  geom_point(data = stations_new, 
             aes(x = long, y = lat, color = percent_casual))+
  labs(title = "Total # of Departures")

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

covid_map_data <- covid19 %>% 
  group_by(state) %>% 
  mutate(state = str_to_lower(state),
         most_recent = max(date)) %>% 
  arrange(date) %>% 
  filter(date == most_recent)

us_map <- map_data("state")

covid_map_data %>% 
  ggplot() +
  geom_map(map = us_map,
           aes(map_id = state, fill = cases)) +
  scale_fill_distiller(palette = "Purples", direction = 1)+
  expand_limits(x = us_map$long, y = us_map$lat)+
  theme_map()+
  theme(legend.title = element_blank())+
  labs(title = "U.S. COVID-19 Case Count by State")

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid_by_pop <- covid_map_data %>% 
  left_join(census_pop_est_2018, by = "state") %>% 
  mutate(cases_per_10000 = (cases/est_pop_2018)*10000)

covid_map_data <- covid19 %>% 
  group_by(state) %>% 
  mutate(state = str_to_lower(state),
         most_recent = max(date)) %>% 
  arrange(date) %>% 
  filter(date == most_recent)

  us_map <- map_data("state")

covid_by_pop %>% 
  ggplot() +
  geom_map(map = us_map,
           aes(map_id = state, fill = cases_per_10000)) +
  scale_fill_distiller(palette = "Blues", direction = 1)+
  expand_limits(x = us_map$long, y = us_map$lat)+
  theme_map()+
  theme(legend.title = element_blank())+
  labs(title = "U.S. COVID-19 Case Count by State per 10000")

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

carData::MplsStops

mpls_suspicious <- MplsStops %>% 
  mutate(sus = problem == "suspicious") %>% 
  group_by(neighborhood) %>% 
  summarise(total_stops = n(),
            prop_sus = sum(sus)/total_stops
            ) %>% 
  arrange(desc(total_stops))

## `summarise()` ungrouping output (override with `.groups` argument)

show(mpls_suspicious)

## # A tibble: 87 x 3
##    neighborhood    total_stops prop_sus
##    <fct>                 <int>    <dbl>
##  1 Downtown West          4409    0.757
##  2 Whittier               3328    0.406
##  3 Near - North           2256    0.415
##  4 Lyndale                2154    0.405
##  5 Jordan                 2075    0.392
##  6 Hawthorne              2031    0.387
##  7 Marcy Holmes           1798    0.288
##  8 Lowry Hill East        1491    0.404
##  9 East Phillips          1387    0.691
## 10 Folwell                1230    0.394
## # ... with 77 more rows

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

colors <- colorFactor("PRGn", 
                      domain = MplsStops$problem)
leaflet(data = MplsStops) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~long,
             lat = ~lat,
             stroke = FALSE,
             weight = .5,
             opacity = 1,
             color = ~colors(problem))

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)
mpsl_sus_to_nbhd <- mpls_nbhd %>% 
  left_join(mpls_suspicious,
            by = c("BDNAME" = "neighborhood"))
mpsl_all <- mpsl_sus_to_nbhd %>% 
  left_join(MplsDemo,
            by = c("BDNAME" = "neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

colors <- colorNumeric("Reds", 
                      domain = mpsl_all$prop_sus)
leaflet(data = mpsl_all) %>%
  addProviderTiles(providers$Stamen.Terrain) %>%
  addPolygons(stroke = FALSE,
              fillColor = ~colors(prop_sus),
              fillOpacity = 0.7,
              label = ~BDNAME) 

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows. How many Starbucks are there in Chicago?

s_starbucks_chicago <- Starbucks %>% 
  filter(City %in% c("Chicago"))

leaflet(data = s_starbucks_chicago) %>% 
  addProviderTiles(providers$Stamen.Terrain) %>% 
  addCircles(lng = ~Longitude,
             lat = ~Latitude,
             weight = 10,
             opacity = 1,
             color = col2hex("lightblue"))

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?